2D texture based classification, segmentation and 3D orientation estimation of tissues using DT-CWT feature extraction methods

Model-based signature verification with rotation invariant features

Based on the combined shape feature and texture feature, a fast object detection method with rotation invariant features is proposed in this paper. A quick template matching scheme based online learning designed for online applications is also introduced in this paper. The experimental results have shown that the proposed approach has the features of lower computation complexity and higher detection rate, while keeping almost the same performance compared to the HOG-based method, and can be more suitable for run time applications.

The issue of target localization by means of texture analysis is addressed. First the texture feature extraction based on multi-channel Gabor filter decomposition and the rotation invariant representation of Gabor features are analyzed in the view of their ability of classification. After that, a method based on Gabor features and neural network classifier is proposed. The method is composed of two stages, unsupervised texture clustering and target localization. In the first stage, original feature space extracted by Gabor filter banks is applied in training a self organized map classifier and a novel merging scheme is presented to achieve the accuracy of clustering. In the second stage, digital Fourier transform of the original feature vectors are applied in back propagation (BP) network to ensure rotation invariance in localization. In the experiments, the usefulness of the proposed method is demonstrated on texture database and practical barcode localization system as well. The method is also proved rotation invariant and accurate in localizing target texture.

An effective rotation and scale invariant log-polar wavelet texture feature for image retrieval was proposed. The feature extraction process involves a log-polar transform followed by an adaptive row shift invariant wavelet packet transform. The log-polar transform converts a given image into a rotation and scale invariant but row- shifted image, which is then passed to the adaptive row shift invariant wavelet packet transform to generate adaptively some subbands of rotation and scale invariant wavelet coefficients with respect to an information cost function. An energy signature is computed for each subband of these wavelet coefficients. In order to reduce feature dimensionality, only the most dominant log-polar wavelet energy signatures are selected as feature vector for image retrieval. The whole feature extraction process is quite efficient and involves only O (n/spl middot/log n) complexity. Experimental results show that this rotation and scale invariant texture feature is effective and outperforms the traditional wavelet packet signatures.

An effective rotation and scale invariant log-polar wavelet texture feature for image retrieval was proposed. The feature extraction process involves a log-polar transform followed by an adaptive row shift invariant wavelet packet transform. The log-polar transform converts a given image into a rotation and scale invariant but row- shifted image, which is then passed to the adaptive row shift invariant wavelet packet transform to generate adaptively some subbands of rotation and scale invariant wavelet coefficients with respect to an information cost function. An energy signature is computed for each subband of these wavelet coefficients. In order to reduce feature dimensionality, only the most dominant log-polar wavelet energy signatures are selected as feature vector for image retrieval. The whole feature extraction process is quite efficient and involves only O (n/spl middot/log n) complexity. Experimental results show that this rotation and scale invariant texture feature is effective and outperforms the traditional wavelet packet signatures.

Orthogonal rotation invariant moments (ORIMs) exhibit attractive characteristics such as rotation invariance (translation and scale invariance can be made after normalization process), robustness against noise, good image reconstruction capability, and low information redundancy. Therefore, these are the most commonly used global techniques for image description in different digital image processing applications. The ORIM features are, however, unable to represent local features. Wavelet transforms (WTs), on the other hand, can effectively represent local features. WTs are not invariant to geometric transformation. We propose hybrid face recognition methods based on ORIMs and WTs. In these methods, WTs are used to represent the local features of face images and also to reduce the dimensionality of the face image which results in reducing the large computational time required for moments. WTs retain the characteristics of the original image even after reducing the size of the image. Moreover, the low frequency subband of WTs provides coefficients which are less sensitive to facial expression variation. The role of ORIMs is to capture highly discriminative rotation invariant global features with minimum redundancy. Detail experimental results show that the proposed hybrid methods based on the combination of WTs and ORIMs outperform the ORIMs methods with low processing time.

Scene classification using semantic description has gained much attention towards automatic image retrieval. In many cases, visual appearance of images get affected by environmental conditions such as low lighting and viewing conditions. Such problems in semantic scenes pose difficult challenges during the classification of sceneries. To address this issue, a new outdoor scene classification method for using low level feature has been proposed in this work. To support automatic scene classification at the concept level an efficient illumination and rotation invariant low level features such as color, texture and edge like features have been used in conjunction with multiclass Support Vector Machine (SVM). In this work, we have taken scene categories like mountains, forests, highways, rivers, buildings etc., from the outdoor scenes for classification experimentation. From the experimental results, we demonstrate that the proposed method provides better classification in the large scale image databases like Eight scene category, upright scene and COREL dataset and gives better performance in terms of classification accuracy.

In this paper, a rotation invariant feature and image normalization based image watermarking algorithm is proposed. The rotation invariant features are extracted. Subregions centered at those feature points are used for watermark embedding and detection. Image normalization is applied to those subregions to achieve the scaling invariance. This image watermarking algorithm is rotation and scaling invariant and the experimental results show its effectiveness.

Invariance against rotation of 3D objects is one of the essential properties for 3D shape analysis. Recently proposed algorithms have achieved rotationally invariant 3D point set analysis by using inherently rotation-invariant 3D shape features, i.e., distances and angles among 3D points, as input to Deep Neural Networks (DNNs). The DNNs capture spatial hierarchy and context among the geometric features to produce accurate analytical results. In this article, we delve further into the DNN-based approach to rotation-invariant and highly accurate 3D point set analysis. In particular, we focus our attention on segmentation of 3D point sets, which is one of the most challenging among 3D point set analysis tasks. We propose a novel DNN for 3D point set segmentation called Rotation-invariant and Multi-scale feature Graph convolutional neural network, or RMGnet. Our RMGnet is more flexible than the previous methods as it accepts as input any handcrafted 3D shape features having rotation invariance. In addition, to accurately segment 3D point sets composed of parts having various sizes, we randomize scales at which handcrafted features are extracted and perform multi-resolution analysis of the features by using the DNN. Experimental evaluation demonstrates high segmentation accuracy as well as rotation invariance of the proposed RMGnet.

There have been much interest and a large amount of research on content based image retrieval (CBIR) in recent years due to the ever increasing number of digital images. Texture features play a key role in CBIR. Many texture features exist in literature, however, most of them are neither rotation invariant nor robust to scale and other variations. Texture features based on Gabor filters have been shown with significant advantages over other methods, and they are adopted by MPEG-7 as one of the texture descriptors for image retrieval. In this paper, we propose a rotation invariant curvelet features for texture representation. With systematic analysis and rigorous experiments, we show that the proposed curvelet texture features significantly outperforms the widely used Gabor texture features. A novel region padding method is also proposed to apply curvelet transform to region based image retrieval. Retrieval results from standard image databases show that curvelet features are promising for both texture and region representation.

This paper presents a thorough investigation into the use of Gabor filters for the extraction of rotation invariant texture features. Numerous experiments have been conducted to discover the effect of different parameter settings on classification results. The optimum parameter settings are established and tested by classification and content based image retrieval experiments on a large database of randomly rotated Brodatz texture images. Resistance of the method to Gaussian noise is also examined. The issues studied in this paper are of great importance for practical applications but have not been adequately addressed by existing work on texture analysis.

This article introduces an effective rotation and scale invariant log‐polar wavelet texture feature for image retrieval. The proposed feature is an attempt to enhance the existing content‐based image retrieval systems that largely present difficulty in coping with images with changes in orientations and scales. The underlying feature extraction process involves a log‐polar transform followed by an adaptive row shift invariant wavelet packet transform. The log‐polar transform converts a given image into a rotation and scale invariant but row‐shifted image, which is then further processed through an adaptive row‐shift invariant wavelet packet transform operation to generate adaptively selected subbands of rotation and scale invariant wavelet coefficients, based on an information cost function. An energy signature is computed for each subband of these wavelet coefficients. To reduce feature dimensionality, only the most dominant log‐polar wavelet energy signatures are selected for the feature vector for image retrieval. The overall feature extraction process is quite efficient and involves only O(n · log n) complexity. Experimental results show that this rotation and scale invariant wavelet feature is quite effective for image retrieval and outperforms the traditional wavelet packet signatures.

Simultaneous Localization and Mapping (SLAM) requires both rotation and scale invariant features. Few algorithms have been developed with rotation and scale invariant features with few limitations. Thus, an algorithm has been proposed to address rotation and scaling invariance. Proposed algorithm Harris-FAST interest point detector is a fusion of Harris and FAST interest point detectors. The detector is optimized by localizing the interest point detector to preferred area, from which the execution time of the algorithm reduced to the order of one fourth. The results of the proposed algorithm are compared with available feature detectors for its performance. Localized-Harris-FAST algorithm has been developed with an interest point detector for use in real time applications like SLAM on a mobile robot, which have limited computational resources.

Compared to ordinary camera images, aerial images captured by UAVs mostly consist of small targets in the distance, with complex image information and rich detail. However, aerial images can be easily affected by factors such as lighting differences and atmospheric conditions, which leads to noticeable color variations between images. Traditional image stitching algorithms applied to drone aerial images often suffer from insufficient accuracy. To improve image stitching accuracy and efficiency, this paper proposes an improved ORB (Oriented Fast and Rotated Brief) feature extraction algorithm. The traditional ORB algorithm has shortcomings in rotation invariance, uneven distribution, and few feature points in the image to be registered. Firstly, this paper enhances the FAST feature point detection algorithm by setting an adaptive threshold based on the image's grayscale value to extract more feature point data. Secondly, the selection strategy of surrounding pixels is optimized during the feature point validation process to reduce the calculation time wasted on false feature points. Finally, the gray centroid method is used to add the main direction to the feature points found by the FAST algorithm to enhance matching accuracy under image rotation. Moreover, this paper optimizes the preprocessing stage of the algorithm to quickly solve the image transformation matrix, and thereby further improve the accuracy of image stitching. The L-M (Levenberg-Marquardt) method is combined with RANSAC to refine the obtained transformation matrix and improve the stitching accuracy. Experimental results demonstrate that the optimized algorithm applied to drone aerial image stitching achieves shorter processing times, higher matching accuracy, and better stitching performance.

Stereo cameras, laser rangers and other time-of-flight ranging devices are utilized with increasing frequency as they can provide information in the 3D plane. The ability to perform real-time registration of the 3D point clouds obtained from these sensors is important in many applications. However, the tasks of locating accurate and dependable correspondences between point clouds and registration can be quite slow. Furthermore, any algorithm must be robust against artifacts in 3D range data as sensor motion, reflection and refraction are commonplace. The SIFT feature detector is a robust algorithm used to locate features, but cannot be extended directly to the 3D range point clouds since it requires dense pixel information, whereas the range voxels are sparsely distributed. This paper proposes an approach which enables SIFT application to locate scale and rotation invariant features in 3D point clouds. The algorithm then utilizes the known point correspondence registration algorithm in order to achieve real-time registration of 3D point clouds.KeywordsPoint CloudScale Invariant Feature TransformIterative Close Point AlgorithmScale Invariant Feature Transform FeatureRANSAC AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.